This model is a fine-tuned version of DatPySci/pythia-1b-sft-full on the DatPySci/iter0 dataset. It achieves the following results on the evaluation set:

Loss: 0.2591
Rewards/real: 0.0604
Rewards/generated: -1.0267
Rewards/accuracies: 0.9460
Rewards/margins: 1.0871
Logps/generated: -570.8114
Logps/real: -468.1696
Logits/generated: 0.2253
Logits/real: -0.2820

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-07
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 8
total_train_batch_size: 64
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/real	Rewards/generated	Rewards/accuracies	Rewards/margins	Logps/generated	Logps/real	Logits/generated	Logits/real
0.2932	0.38	300	0.2962	0.0718	-0.7855	0.9220	0.8572	-568.3989	-468.0556	0.2554	-0.2530
0.2689	0.77	600	0.2591	0.0604	-1.0267	0.9460	1.0871	-570.8114	-468.1696	0.2253	-0.2820

Framework versions

Transformers 4.38.1
Pytorch 2.2.1
Datasets 2.17.1
Tokenizers 0.15.2

DatPySci/pythia-1b-kto-iter0

pythia-1b-kto-iter0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

DatPySci/
pythia-1b-kto-iter0